Machine-learning-enhanced quantum sensors for accurate magnetic field imaging
This work addresses the challenge of accurate magnetic field imaging for characterizing nano- and micro-materials, representing an incremental improvement by integrating machine learning with existing quantum sensing techniques.
The researchers tackled the problem of accurately deducing magnetic fields from nanodiamond ensembles (NDEs) by combining NDEs with machine learning, achieving a high accuracy of 1.8 μT without relying on physical models. They also discovered field direction dependence in NDE signals, enabling potential applications in vector magnetometry and visualization of mesoscopic currents in materials like atomic-layer structures and living organisms.
Local detection of magnetic fields is crucial for characterizing nano- and micro-materials and has been implemented using various scanning techniques or even diamond quantum sensors. Diamond nanoparticles (nanodiamonds) offer an attractive opportunity to chieve high spatial resolution because they can easily be close to the target within a few 10 nm simply by attaching them to its surface. A physical model for such a randomly oriented nanodiamond ensemble (NDE) is available, but the complexity of actual experimental conditions still limits the accuracy of deducing magnetic fields. Here, we demonstrate magnetic field imaging with high accuracy of 1.8 $μ$T combining NDE and machine learning without any physical models. We also discover the field direction dependence of the NDE signal, suggesting the potential application for vector magnetometry and improvement of the existing model. Our method further enriches the performance of NDE to achieve the accuracy to visualize mesoscopic current and magnetism in atomic-layer materials and to expand the applicability in arbitrarily shaped materials, including living organisms. This achievement will bridge machine learning and quantum sensing for accurate measurements.